where \(\mathbf {\Theta }_{ij}\in \mathbb {R}^{2}\)  is the unnormalized probability. The first dimension indicates the probability of positive, and the second dimension indicates the probability of negative.
\(\mathbf {G}^i\)  is the global feature of time series \(i\) , which is obtained by a convolutional network \(\mathbf {G}^i=\text{FC}(\text{vec}(\text{Conv}(\mathbf {S}^i_{train})))\) , where
\(\mathbf {S}^i_{train}\in \mathbb {R}^{L_{train}}\)  is the entire sequence \(i\)  over training dataset, and \(L_{train}\)  is the length of the training dataset.
\(\mathbf {S}^i_{train}\)  is static for all samples during training, helping to make the training process more robust and accurate.
The feature \(\mathbf {H}^i\)  is dynamic for different training samples to reflect the dynamics of dependency graphs [1]}.
As such, we use the cross-entropy between \(\mathbf {\Theta }\)  and the \(k\) NN graph \(A^a\)  as graph structure regularization:
\(\mathcal {L}_{graph} = \sum _{ij}-\mathbf {A}_{ij}^a\log \mathbf {\Theta }^{^{\prime }}_{ij}-(1-\mathbf {A}_{ij}^{a})\log (1-\mathbf {\Theta }^{^{\prime }}_{ij}),\) 
